<p><b>Abstract</b>—Computer vision algorithms are natural candidates for high performance computing systems. Algorithms in computer vision are characterized by complex and repetitive operations on large amounts of data involving a variety of data interactions (e.g., point operations, neighborhood operations, global operations). In this paper, we describe the use of the custom computing approach to meet the computation and communication needs of computer vision algorithms. By customizing hardware architecture at the instruction level for every application, the optimal grain size needed for the problem at hand and the instruction granularity can be matched. A custom computing approach can also reuse the same hardware by reconfiguring at the software level for different levels of the computer vision application. We demonstrate the advantages of our approach using Splash 2—a Xilinx 4010-based custom computer.</p>